The Performance of Emerging Hedge Funds and Managers

Transcription

1 USC FBE FINANCE SEMINAR presented by: Philippe Jorion FRIDAY, Oct. 30, :30 am - 12:00 pm, Room: ACC-201 The Performance of Emerging Hedge Funds and Managers Rajesh K. Aggarwal and Philippe Jorion* August 7, 2009 * Aggarwal is with the Carlson School of Management, University of Minnesota. Jorion is with the Paul Merage School of Business, University of California at Irvine and Pacific Alternative Asset Management. We thank Mufaddal Baxamusa and Yihui Pan for research assistance. We thank the referee for very detailed and useful comments. The paper has also benefited from the comments and suggestions of Jim Berens, Jane Buchan, Will Goetzmann, Iwan Meier, Judy Posnikoff, and seminar and conference participants at UC-Irvine, the American Finance Association Annual Meetings, and the CFS Conference on Asset Management and International Capital Markets. Correspondence can be addressed to: Rajesh K. Aggarwal Philippe Jorion Carlson School of Management Paul Merage School of Business University of Minnesota University of California at Irvine Minneapolis, MN Irvine, CA (612) (949) aggar015@umn.edu pjorion@uci.edu 2009 Aggarwal and Jorion

2 The Performance of Emerging Hedge Funds and Managers ABSTRACT This paper provides the first systematic analysis of performance patterns for emerging funds and managers in the hedge fund industry. Emerging funds and managers have particularly strong financial incentives to create investment performance and, because of their size, may be more nimble than established ones. Performance measurement, however, needs to control for the usual biases afflicting hedge fund databases. After adjusting for such biases and using a novel event time approach, we find strong evidence of outperformance during the first two to three years of existence. Each additional year of age decreases performance by 42 basis points, on average. Cross-sectionally, early performance by individual funds is quite persistent, with early strong performance lasting for up to five years. JEL Classifications: G11 (portfolio choice), G23 (private financial institutions), G32 (financial risk management) Keywords: hedge funds, emerging managers, incentives, performance evaluation

3 I. Introduction The hedge fund industry has grown very rapidly. Assets under management have increased from an estimated $39 billion in 1990 to more than $1.8 trillion in Correspondingly, the number of funds has increased from 610 to more than 10,000. One immediate question with the large growth in the number of funds is whether all of these new managers and funds are capable of generating superior performance. This paper provides the first systematic evidence on whether emerging hedge funds and managers tend to outperform more established ones. We find that emerging funds and managers tend to add value in their early years. Thereafter, performance tends to deteriorate. Each additional year of fund age decreases fund performance by 42 basis points on average, although as we show below, this relation is better described as nonlinear. This result suggests that emerging funds, especially in the first two years of life, represent attractive investment opportunities. We also find that, at startup, larger funds run by multi-fund management companies tend to perform better. These results are consistent with emerging funds and managers having stronger incentive effects, and those that start up with a larger pool of capital and existing organizational infrastructure are able to capitalize on these advantages. The growth of the hedge fund industry can be rationalized by the value added generated by hedge fund managers that we document. For example, over the period 1994 to 2007, the CSFB hedge fund index delivered an additional 6.7% annual return over cash. 2 Put differently, this performance is slightly greater than that of the S&P stock market index over the same period, but with half the volatility and very little systematic risk. These performance results are puzzling in view of the mutual fund literature, which finds that mutual funds generally fail to outperform their benchmarks even after adjusting for risk. Hedge funds, however, differ in a number of essential ways from mutual funds. 1 According to the HFR (2008) survey, excluding funds of funds to avoid double-counting. 3

4 They provide more flexible investment opportunities and are less regulated. More flexible investment opportunities include the ability to short securities, to leverage the portfolio, to invest in derivatives, and generally to invest across a broader pool of assets. The lighter regulatory environment creates an ability to set performance fees, lockup periods, and other forms of managerial discretion such as limited reporting. Hedge fund managers also have a stronger financial motivation to perform because of the compensation structure typical of hedge funds: this includes both a fixed annual management fee that is proportional to assets under management and an incentive fee that is a fraction of the dollar profits. In addition, hedge fund managers often invest a large portion of their own wealth in the funds they manage, while many mutual fund managers never invest in their own funds (Kinnel 2008). At the same time, there is a fair amount of interest in emerging managers and funds, defined as newly-established managers and funds. In this paper, we examine both emerging hedge fund managers and emerging hedge funds, using fund age or years of existence since inception as the primary sorting criterion. 3 We focus on emerging funds and managers for several reasons. Incentive effects should be stronger for this class of hedge fund managers. The marginal utility of a given annual profit should be higher for managers with lower initial wealth; given that emerging managers are likely to be on average younger than more established managers, profits can be expected to accrue over a longer lifetime. In addition, an existing manager starting a new fund has strong incentives to perform for both reputational reasons and to gather more assets for both the new and existing funds. Further, because of their size, emerging funds may be more nimble than established funds. Finally, emerging funds are more likely to be open to investors than established hedge funds, especially established funds with strong historical performance. 2 The CSFB hedge fund index, which in absolute terms returned 10.9% per year over this period, is representative of all hedge funds and does not represent the returns on just emerging hedge funds. For emerging hedge funds, see Table 1. 4

5 This is the first academic paper to focus on emerging funds and managers. Age sometimes appears as another factor explaining performance with generally insignificant effects. Crucially, the age factor in hedge funds is subject to a very significant backfill bias. This bias occurs because managers report their performance to the databases only voluntarily there is no requirement that managers disclose performance. Typically, after inception, the fund s performance is not made public during some incubation period. Upon good performance, the manager is more likely to make the performance public. If so, the manager starts reporting to the database current performance and backfills the past performance, and not even necessarily over the entire incubation period. Funds that collapse due to poor performance may never appear in the database. Our paper eliminates backfill bias by selecting a sample of funds with inception dates very close to the start dates in the database. We find that backfill bias would otherwise completely distort measures of early performance, imparting an upward bias of around 5% in the first three years. The common practice of arbitrarily dropping the first 12 or 24 months of the sample is insufficient to control for backfill bias. In addition, it may bias tests of persistence toward nonrejection because performance during the backfill period generally appears very high. Our paper provides evidence on whether emerging hedge funds and managers tend to outperform more established ones. We examine fund performance in event time where the event is the start of fund performance. Examining funds in event time is a more powerful and direct method to assess the relationship between age and performance. To see this, suppose that every year a large number of new funds start up, and that new or emerging funds outperform existing funds. Running pooled cross-sectional regressions of fund returns on indices or factors (even with time fixed effects) in calendar time would imply that hedge funds outperform on average. 3 Initially, we take recently established funds as a proxy for emerging managers. It is possible, however, that a recently established fund is run by a manager who has run other hedge funds. Later, in Section IV, we separately evaluate 5

6 However, the outperformance is actually generated by the new funds, an effect which will be captured in event time but missed in calendar time. Our use of event time is novel in hedge fund research, and the event time approach is ideally suited for examining the performance of emerging hedge funds. Conventional event studies typically examine short horizon reactions to news or events. More recently, long horizon event studies have been used to examine differences in firm returns due to changes that cannot precisely be pinned down to the day. Our use of event time is long horizon in nature we examine hedge fund performance over years but we know precisely when the hedge fund starts reporting performance. We use event time to measure hedge fund aging, which is similar to a cohort analysis, while still allowing us to create portfolios of hedge funds. Using portfolios allows us to test hypotheses while automatically accounting for correlations in returns across funds. This is because the standard errors we report are based on portfolio returns. In contrast, pooled cross-sectional regressions of individual fund returns are usually mis-specified due to cross-sectional correlations in fund returns. Our econometric approach yields robust evidence that emerging funds and managers tend to add value in their early years. This result holds regardless of organizational form (single product versus multi-product management companies) and also regardless of whether the manager has previously run another fund. In addition, when we form portfolios of emerging funds, we find that early performance (up to five years) is persistent. The persistence we find is present both for the best performing quintile and the worst performing quintile of hedge funds. This result is important because earlier studies of performance persistence tend to find performance persistence amongst only the worst performing funds. As hedge funds become more established (i.e., age) the performance persistence that we document fades away, along with the outperformance exhibited in the funds early years. managers of recently-established funds who have previously run a fund listed in TASS and those that have not. 6

7 In further tests, we perform a cohort analysis, where we track over time all funds that start within a given year. This analysis allows us to more precisely control for changes in fund size. One possibility is that past good performance may lead to inflows, which results in the deterioration of fund performance over time, as hypothesized by Berk and Green (2004). Under these conditions, the deterioration in fund performance is actually due to changes in fund size, and not fund age. When we control for fund size, we continue to find that younger funds perform better and this performance deteriorates over time. In addition, we find that, at startup, larger funds run by multifund management companies perform better, suggesting advantages to scale associated with organizational infrastructure. This paper is structured as follows. We review the rationale for emerging funds and managers and relevant literature in Section II. Section III then describes the data and empirical setup. Section IV discusses the results. Concluding comments are contained in Section V. II. The Rationale for Emerging Managers Emerging funds and managers may be attractive for a number of reasons. The first set of arguments is related to incentive effects. There are good reasons to believe that incentive effects are particularly important for the hedge fund industry. Incentives should help sort managers by intrinsic skills. We would expect the best asset managers to migrate to the hedge fund industry. In addition, incentives should induce greater effort by managers, as predicted by agency theory (e.g., Jensen and Meckling (1976)). In the mutual fund industry, Massa and Patgiri (2007) compare the usual fixed management fee setup with arrangements where this fee decreases as a function of asset size. This concave function provides a negative incentive effect, which is found to be associated with worse performance, as predicted. In the hedge fund industry, Agarwal et al. (2007) find that greater managerial incentives, managerial ownership, and managerial discretion are associated with superior 7

8 performance. In addition, these effects explain the empirical evidence of return persistence for hedge funds, while little persistence has been reported for mutual funds. 4 Relative to more established and older managers, incentive effects should be even more important for emerging managers because their initial wealth is smaller. The marginal utility of the same dollar amount of fees should progressively decrease as the manager gets richer. In addition, the benefits of high-powered incentive contracts carry over a longer period, since emerging managers are generally younger. So, emerging managers should put more effort into enhancing performance. In their starting years, managers may also be more focused on generating performance rather than spending time marketing to new investors. In addition, an existing manager starting a new fund has strong incentives to perform for both reputational reasons (the new fund has no pre-existing track record) and to gather more assets for both the new and existing funds. The second set of arguments for emerging managers is related to size. They generally manage a smaller asset pool than the typical fund. Goetzmann et al. (2003) argue that arbitrage returns may be limited, leading to diseconomies of scale. They report that, in contrast with the mutual fund industry, large hedge funds frequently prefer not to grow. Diseconomies of scale also underpin Berk and Green (2004) s model that explains many regularities in the portfolio management industry that are widely regarded as anomalous. Managers with skill attract inflows, but diseconomies of scale erode performance. As a result, the performance of skilled managers disappears over time. Getmansky (2004) studies competition in the hedge fund industry and finds decreasing returns to scale. 4 Jagannathan et al (2007) find evidence of persistence in hedge fund returns over 3-year horizons. They also provide a review of the literature on persistence in hedge fund returns. Kosowski, Naik, and Teo (2007) report mild evidence of persistence using classical OLS alphas but much stronger evidence in a Bayesian analysis. Baquero et al. (2005) report persistence at the quarterly and annual horizons, using raw and style-adjusted returns. Aggarwal, Georgiev, and Pinato (2007) show performance persistence for time horizons ranging from six months to over two years. Carhart (1997) reports no evidence of persistence in mutual fund returns using abnormal returns defined by a 4-factor model. These conclusions are reinforced by Carhart et al. (2002), who deal with survivorship and look-ahead biases for mutual funds. 8

9 For mutual funds, however, the evidence is mixed. Grinblatt and Titman (1989) and Wermers (2000) find no significant difference across the net performance of small and large funds. Chen et al. (2004) report some evidence of a negative relationship between fund returns and size, but this is exclusively confined to funds that invest in small stocks, which tend to be illiquid. This is confirmed by Allen (2007), who reports no difference across size for institutional investors except for the small cap category, which is capacity-constrained and for which small funds perform better. Another set of arguments for emerging managers is that they may have newer ideas for trades, whose usefulness can fade away over time. New funds may be established to take advantage of new markets or financial instruments. Irrespective of a performance advantage, emerging funds are often open to new investors and, therefore, represent practical investment opportunities in hedge funds. So far, no academic paper has directly investigated the performance of emerging hedge fund managers. 5 Age sometimes appears as another factor explaining performance in mutual funds, with generally insignificant effects. In addition, the age factor is subject to a very significant backfill bias or instant-history bias with hedge funds. This bias arises from the option to report performance or not, and if so, to backfill performance produced during an incubation period. To evaluate the effect of age, it is crucial to control for backfill bias, which would otherwise make early returns look better. Our analysis controls for both backfill and survivorship biases. The age effect that is the focus of our study is also related to the literature on career concerns of portfolio managers. For mutual funds, Chevalier and Ellison (1999) indicate that termination is more sensitive to performance for younger managers. Combined with the incentive structure in this industry, they argue that this should lead to less risk taking in younger managers. This is confirmed by their data. Given the vastly different incentives schemes, it is not clear 5 Some industry studies suggest that young funds perform better. For example, Howell (2001) claims that young funds outperform old funds by 970 basis points on average but fails to control for backfill bias (see also Jones (2007)). 9

10 whether these results should carry over to the hedge fund industry, however. Boyson (2008) finds that young hedge funds have marginally greater performance than old hedge funds. Her sample, however, does not focus on new or emerging managers. III. Data and Setup A. Database The database employed has been collected by Tremont Advisory Shareholders Services (TASS), which compiles fund data over the period November 1977 to December The TASS database covers close to one-half of the estimated total number of hedge funds in existence. The database provides total monthly returns net of management and incentive fees, as well as assets under management (AUM). For our analysis, we use data starting in January 1996, the first year for which there is a non-trivial number of non-backfilled funds, using the method defined below. TASS reports two separate databases, one with live funds and another with graveyard funds, which keeps track of dead funds and starts in Many funds stop reporting at some point, because of liquidation or some other reason. We include the graveyard database to minimize survivorship biases. We eliminate funds of funds as well as duplicate classes from the same fund family. In addition, we only retain funds that provide returns in U.S. dollars and net of fees. While eliminating duplicate classes and funds providing returns in currencies other than US dollars is sufficient to eliminate most situations of the same fund appearing multiple times in the data, it does not completely resolve the problem. For example, two funds can appear in the database, be run by the same manager, and have the same name up to one fund having the designation onshore and the other having the designation offshore. As another example, two funds can have the same manager and the same name up to one fund being an LP (limited partnership) and the other being limited or an investment company. These situations often 10

11 happen in fund companies set up with a master-feeder fund structure, where multiple feeder funds channel capital to one investing master fund. In these situations, if the funds are duplicates (for example, if the returns are 0.99 correlated or more), we eliminate one of the duplicates. 6 More precisely, funds are selected as follows. If two (or more) funds from the same management company have a duplicate series of returns for the months in which both report, and one fund started later than the other, then we keep the oldest fund or the fund with the longest return series. If two funds have duplicate series for the months in which both report and one fund stops reporting before the other, we again keep the fund with the longer return series. If two funds have duplicate series and exactly the same months for which they report, we keep the larger of the two funds by initial assets under management (AUM). In order to avoid double-counting assets, we retain the assets under management only for the fund whose return series we include. This is because, in many cases, the smaller fund is a feeder fund for the larger master fund, which implies that the feeder funds AUM could already be included in the master funds AUM. On the other hand, situations may exist without double-counting of assets (e.g., side-by-side onshore and offshore funds or side-by-side funds denominated in different currencies); in such cases, we will understate the aggregate AUM for that manager. B. Constructing Emerging Fund Returns It is well-known that the practice of backfilling returns causes severe biases in performance measurement. 7 Fung and Hsieh (2000) report that the median backfill period is about 12 months 6 We note that much of the existing literature does not correct for such master-feeder duplicates, thereby imparting another potential bias to hedge fund research. 7 Evans (2007) also reports a substantial incubation bias for mutual funds. Apparently, mutual fund families seed new funds without initially making their performance public. After a while, the fund may acquire a ticker symbol from the NASD, thus becoming public. He defines a fund as incubated if the period between the ticker creation date and the fund inception date is greater than 12 months. He reports a difference in performance of 4.7% between incubated funds during their incubation period and an age-matched sample of non-incubated funds. 11

12 based on the TASS database from 1994 to They adjust for this bias by dropping the first 12 months of all return series and report a bias estimate of 1.4% per annum (see also Malkiel and Saha (2005)). The common practice in hedge fund academic research is to drop the first 12 or 24 months to control for backfill bias (e.g., Kosowski, Naik, and Teo (2007)). This adjustment, however, is peculiar. For funds with no backfill, this discards the first year of performance, which is perfectly valid and very informative. Moreover, for the 50% of funds with backfill longer than 12 months, this still preserves backfill bias. Whether this biases the results of the empirical analysis depends on the research objective. Clearly, backfill bias is of first-order importance when evaluating the initial performance of emerging funds. A better method to control for backfill bias is to minimize the period between inception of the fund and the first entry date into the database. Thus, we focus on the funds for which there is no (or very little) backfill bias. TASS provides an inception date, a performance start date, as well as a date added to database. The inception date is the inception date of the legal fund structure and generally will not coincide with the start of actual fund investment and performance. The performance start date is the date of the first reported monthly return. The date added to TASS is when the fund chooses to start reporting to TASS. For a typical fund in the TASS database, the inception date is prior to the performance start date, and the performance start date is prior to the date added to the database. Backfill occurs when the performance start date is before the date the fund was added to the database. The difference is the backfill period. We find that the median backfill period in the entire database is 480 days, which is substantial. In addition, 37% of funds have a backfill period longer than two years; 25% of funds have a backfill period longer than 1165 days, which is more than three years. The obvious concern here is that funds only choose to report to TASS if past performance has been good and this performance is then allowed to be backfilled. 12

13 To control for this effect, we separate the sample into a non-backfilled sample and a backfilled sample. We define a fund as non-backfilled if the period between the inception date and date added to the database is below 180 days. Note that this definition slightly differs from the traditional definition of the backfill period, which is the difference between the performance start date and the date added to the database. Our definition takes into account the possibility that funds may have actual performance that they choose not to report between the inception date and the performance start date (which is self-chosen and reported). Our focus on the difference between the inception date and the date added to the database minimizes both backfill bias and the possibility of omitted performance. At the same time, the lag of 180 days is required because very few funds report to the TASS database immediately at inception. For most funds, not reporting performance at inception is entirely legitimate as the fund may be gathering assets rather than investing. The remaining period between the performance start date and date added to TASS is minimal, with a median of 82 days. 8 As we show later, our results are not driven by this window. C. Time Alignment We perform two types of analysis, based on event time and cohort by calendar year. In the first type, the event is the start of fund performance. We form an equally-weighted portfolio of funds aligned on the first month of reported performance. Equal-weighting generates the expected return from a strategy of picking all managers meeting the relevant characteristics. To transform to yearly returns, we cumulate the first 12 months of performance, which is called year 1. The second twelve month period is then called year 2, and so on. From January 1996 to December 2006, we 8 Our use of a window of 180 days for non-backfilled funds is in fact much more stringent than the 12-month window used by Evans (2007) in the context of mutual fund incubation bias. 13

14 have at most 132 months in event time, which could only be achieved by a fund starting in January 1996 that survives until December 2006 (there are three such funds). Panel A in Table 1 reports raw returns for our portfolio of emerging funds. We have 923 funds that start (beginning of event year 1) over the period 1996 to 2006 that are free of backfill bias. By the beginning of event year 2, this number falls to 749, due to fund attrition as well as truncation at the end of the sample (i.e., a fund that starts in 2006 will not have two years of performance). By the beginning of event year 9, there are only 44 funds in the portfolio. The last two years are not reported in the table due to the small number of data points. This process leads to the largest number of funds in the first month, as every fund in the database with no backfill has at least one month of performance, and a smoothly decreasing number of funds in event time. Note that the first year performance for these funds is substantially higher than for subsequent years. The average raw return is 12.16% in the first year. This falls to 7.99% in the second year, which suggests outperformance by emerging managers. During the first two years, average performance is 10.1%, versus 9.1% during the remaining seven years. Panel A also displays the portfolio volatility, annualized from monthly data. Volatility is very low given the large number of funds in the portfolio and the aggregation process across time. As event time goes by, the volatility increases due to the smaller number of funds in the portfolio. This volatility is also the standard error of the annual return. 9 Thus the estimated annual return becomes less reliable as time goes by. The table also shows the t-statistic that tests equality of consecutive annual returns. The return drop from the first to the second year is significant. Note that what matters is not the level nor sign of returns but rather their patterns over time. Performance is measured relative to the universe of funds, assuming all returns are drawn from the same 9 Statistics are first computed from monthly returns (mean m and volatility m ). The annual return is a =12 m and the annual volatility is a = 12 m. 14

15 distribution. Later, we will make adjustments for risk and contemporaneous correlations across funds. The last column in Table 1 reports the typical fund (not portfolio) volatility, taken as the cross-sectional average of this risk measure across all the funds in this group. Fund volatility is slightly higher in earlier years. Given our interest in emerging managers, we need to correct for backfill bias in the TASS data. To illustrate this point, Panel B in Table 1 repeats the analysis using all emerging managers in the TASS data from 1996 to 2006 that do not meet our definition for no backfill. In other words, these are the backfilled funds. The returns are event time returns, which are therefore comparable to the returns in Panel A. There are many more backfilled funds than non-backfilled funds, 2267 initially versus 923. Backfilled funds exhibit much higher returns for the first four years than nonbackfilled funds. The extent of backfill bias is substantial. During the first year, this bias is 6.44%, which is the difference between 18.60% and 12.16%. This bias persists for the first four years, dropping to 4.4%, 4.5%, and 2.4% in the years after the first year. The difference is significant at the one-sided 95% level during each of the first four years. Our second type of time alignment groups funds according to the calendar year in which they start. A cohort is defined as a group of funds that start reporting during each of the years in our sample, from 1996 to For example, we have 74 funds with inception date and performance data starting during 1996 with no backfill. Each month, we construct an equallyweighted portfolio of returns across all funds for which we have data. Summing, this gives the average performance for that cohort (e.g., 1996) in year t, R, t Note that, unlike the event-time analysis, there are fewer funds in January, which means that the weight of each fund and portfolio 15

16 variability will be greater. 10 The size of each cohort successively shrinks as years go by; for example, the 1996 cohort decreases from N 1996,1 =74 to N 1996,2 =69 in January, 1997, and to only N 1996,11 =10 in January, To get the average return for the first year of our 11 cohorts, we take: R 1 11 ( 1996,1 1997,1 R2006, 1 1 R R... ) (1) The average return for the second year of our cohorts averages the second years of our funds ( R cohortyear, 2 ), i.e., 1997 returns for the funds started in 1996, 1998 returns for the funds started in 1997, and so on. We do this for all years up to the maximum of 11 years. This cohort/calendar time analysis provides an alternative method of classifying funds. It also allows us to sort by size on an annual basis, which provides a more natural sorting for size than does the event time analysis. The results of this method will be presented later in Table 6, when discussing size effects. D. Performance Measures We use several measures of performance. The first measure is the raw return, as previously discussed. The advantage of this method is that it does not require estimation of any parameter. However, it does not control for risk or market movements. The second measure uses the TASS classification into one of twelve sectors. Of these twelve sectors or styles, funds in our sample belong to ten: convertible arbitrage, fixed income, event driven, equity market neutral, long-short equity, short bias, emerging markets, global macro, managed futures, and multi-strategy. For each sector, CSFB provides an index based on an asset-weighted portfolio return of funds selected from 10 For example, there are 13 funds in operation in January 1996, so the January 1996 portfolio return is an equal weight average of the 13 funds returns. There are 17 funds (13 one-month old funds and 4 new funds) in operation in 16

17 the TASS database. These CSFB indices include funds with at least one year of track record, with at least $10 million in assets, and with audited financial statements. 11 These indices should be free from backfill and survivorship biases, because they are constructed live, or from contemporaneous data. 12 Indeed, these indices are not recomputed to include previous returns and do include funds that may die later. We use these sector returns to adjust fund returns for sector effects. Abnormal, styleadjusted, returns are measured as: AR S it R R, (2) it it St where R it is the return on fund i at time t, R St is the return on the sector S to which fund i belongs, and it is the sector exposure of fund i, computed over two calendar years or less if the series are shorter. To be specific, the exposure it for years 1 and 2 is calculated using all of fund i s return data from years 1 and 2; thereafter, it is calculated using return data from years t and t The advantage of this approach is that it is simple to implement. It controls for sector effects, which is appropriate when comparing performance across funds. It also adjusts for general movements in fund returns, such as the period of negative returns experienced during the third quarter of 1998, at the time of the Long-Term Capital Management crisis. As a result, the variance February 1996, so the portfolio return is an equal weight average of the 17 funds returns. The number of funds increases during the calendar year. 11 After April 2005, the minimum size went up to $50 million. 12 These indices were constructed live since December Prior to that, however, the returns may have been backfilled. In addition, as Ackermann et al. (1999) indicate, a remaining bias might exist, called liquidation bias. This arises if a fund stops reporting and falls further in value thereafter. The authors indicate that the index providers take great pains to ensure that the final return is included. Even when not included, their paper reports that the remaining loss in value is estimated at minus 0.7%, which is small. For instance, the Bear Stearns High Grade Structured Credit Fund failed during June The June performance for this fund was announced too late to be included in the June returns for the index, but was included in the July index returns. The effect was small, however, because this fund had a weight of less than 0.2% in the broad index. For our purposes, the last month of performance was eventually included in the database. 13 We have also performed the analysis using betas calculated over one year. The results are quite similar to all of those reported below. The advantage of using one year betas is that abnormal returns can be calculated out-of-sample (i.e., using the prior year s beta) in all years except the first year. The disadvantage is that the betas are noisier. 17

18 of abnormal returns should be less than that of raw returns, which should increase the power of the tests. This approach also controls for risk, taken as a factor exposure. For instance, keeping the correlation fixed, a fund with higher leverage should have higher volatility and hence higher beta. On the other hand, the classification into sectors may be arbitrary. This can be an issue with funds that straddle several strategies, or with funds that change their investment themes over time. Note that this approach simply provides a measure of relative performance with respect to other funds with the same style. Because hedge funds are not compared to other asset classes, a negative alpha does not mean that a fund has poor absolute return performance. 14 One other concern with this approach is that we must estimate the abnormal returns and the betas (sector exposures) insample. In other words, there is no estimation period followed by a predictive period. Given our focus on emerging hedge fund managers (who, by definition, do not have past returns), this is simply a cost of making an adjustment for risk. More generally, the approach we use is the approach taken in most hedge fund research on performance and performance persistence (see, e.g., Jagannathan, et. al. (2007) and Kosowski, et. al. (2007)) which typically involves short time series. IV. Empirical Results A. Style-Adjusted Performance Using Event Time We start by presenting style-adjusted returns for the event-time portfolio. Panel A in Table 2 presents alphas by age, ranging from one to nine years after inception. Panel A shows that first- 14 We have also examined the Fung and Hsieh (2004) asset-based style (ABS) factors, with betas estimated over the entire period, with similar results. We choose not to report the Fung and Hsieh results because a large number of new funds stop reporting fairly quickly, creating unstable estimates. For example, of our 923 funds, 174 stop reporting within twelve months. 68 of these funds start in 2006 and survive, but cannot report more than twelve months of performance; the rest stop reporting mostly due to failure and liquidation. Estimating Fung and Hsieh seven factor exposures with less than 12 months of data is clearly problematic because we over-fit alphas. On the other hand, dropping these funds results in the elimination of almost 20% of our sample. Nonetheless, when we drop funds with fewer than 12 months of performance, we find results based on the Fung and Hsieh factors that are similar to the results reported based on style indices. 18

19 year alphas are 4.31%, falling substantially in years two to five, and then varying between positive and negative values thereafter. 15 Standard errors are systematically smaller than in Table 1, reflecting the increase in power due to the sector adjustment. The test column presents the t-statistic for the hypothesis of no change in annual return. The first-year drop is statistically significant. Performance continues to drop in the third and fourth years. To summarize the outperformance, the average alpha during the first four years is 1.57% per annum, versus 0.36% during the next five years. Emerging managers, narrowly defined over a time span of two years, generate an abnormal performance of 2.71% per annum relative to 0.38% later. 16 This difference is statistically and economically significant. The last column reports the typical fund beta, taken as the arithmetic average of this risk measure across all the funds in this group. Average betas are around 0.7 relative to the style indices. This beta differs from unity because the typical fund may not be perfectly correlated with the style factor (e.g., the manager has new ideas), or may not have the same leverage or volatility. 17 Panel B presents results from a regression of monthly portfolio alphas on a time trend. In the first column, we include a linear time trend. Each additional month of age decreases monthly performance by 0.29 basis points. Each additional year of age would decrease monthly performance by 0.29*12 = 3.5 basis points or yearly performance (annual alpha) by 3.5*12 = 42 basis points, on average. The time trend is even stronger and more statistically significant when 15 To address concerns that there may be some residual backfill bias in our results, we have also examined the funds for which there is no backfill at all because they are added to TASS within 30 days of their first performance report. There are initially 243 such funds. The patterns we describe for the full no backfill sample are similar for this sample as well, but with less statistical power due to the smaller number of funds. In the first year in event time, the average alpha is somewhat smaller at 2.31% with a standard error of 1.19%. 16 We have also assigned each of our hedge funds to one of ten sectors and formed a portfolio of the hedge funds in that sector. The largest sector is long-short equity, with 40.4% of the funds at inception. The results are not driven by any one sector. Most sectors display a decline in alphas in event time, including long-short equity. We do not report statistics for this decomposition because the number of funds is small for each sector and shrinks very quickly in event time, which creates more variability in the average alphas. 19

20 using the logarithm of age as the explanatory variable, as shown in the second column. 18 Thus, emerging managers display significantly better performance during their initial years. Hedge funds can differ in their organizational form. Some hedge funds are run by large, multi-product fund management companies while others are run by a management company dedicated to that sole hedge fund. It is possible that single-fund management companies have stronger incentives to succeed as the managers attention and efforts can be concentrated on a single fund. Conversely, for reputational spillover reasons, managers at multi-product management companies may have strong reasons to curtail poor performance and shut down underperforming hedge funds. Table 3, Panel A examines whether differences in organizational form matter for abnormal fund performance. The left sub-panel presents alphas for funds from multi-fund management companies; the right sub-panel presents alphas for funds from single-fund management companies. Most of the funds that start (576 out of 923 at the outset) belong to multifund management companies. 19 Performance is similar across funds from the two types of organizations, albeit slightly worse for funds from single-product management companies. This difference is consistent with the findings by Chen, et al. (2004) that mutual fund performance slightly increases when a fund is part of a larger fund group, probably due to economies of scale in trading commissions and marketing costs. Up until this point, our focus has entirely been on emerging or new funds. However, it is likely that many managers of new funds have run other hedge funds before. For example, it is easy 17 There does appear to be some tendency for beta to increase over time, which parallels the decrease in alpha. This is in part due to the negative correlation between alpha and beta due to the overlap in estimation period. Because style indices tend to have positive returns, if beta is underestimated, this will lead to an overestimation of alpha. 18 We find similar results using the alphas from the Fung-Hsieh seven factor model. 19 In an earlier version of this paper, we defined funds as belonging to multi-fund management companies if at any time the management company ran multiple funds. This definition allowed for the possibility that a management company might start a fund, generate strong performance, and then start additional funds. The initial fund would have been classified as belonging to a multi-fund management company. We now classify new funds as belonging to a multi-fund 20

21 for a successful manager to close a fund, perhaps because of a regulatory limit on the number of investors, and then to start a new fund with a similar investment strategy. In this case, the manager should not truly be considered emerging. In order to address this concern, we verify whether the manager (or any of the managers) of a fund in our sample has run any other funds that report to TASS. This approach, admittedly, does not account for managers that previously ran another fund not reported to TASS. Using this classification scheme, we find that 397 of our sample of 923 funds have managers who have previously run another fund in TASS. There are 493 funds run by managers without a previous record in TASS. 20 The remaining 33 funds do not report managers names and are excluded. Table 3, Panel B compares the alphas of managers who have and have not previously run another fund in TASS. Generally, the time patterns are similar for both groups. Managers who have previously run another fund have slightly higher abnormal performance than managers who have not previously run other funds. The first few years, however, are very similar. Overall, emerging funds seem to perform better in earlier years, and this is true as well of emerging managers who have not previously run another hedge fund, defined to the best extent possible with the available data. Panel C provides regression results to support the previous findings. Sub-panel 1 shows that the age effect is significant for both single and multi-funds, with a stronger effect for single funds. Sub-panel 2 shows that the age effect is significant and equally strong for managers who have and have not previously run a fund in TASS. As an additional test, we consider separately the live and dead funds from TASS. Out of the total sample of 923 funds, 493 funds died during our sample management company only if there are other funds run by the management company on or before the inception date of the new fund. All of our results are robust to this change in definition. 21

22 period. Sub-panel 3 shows that the age effect is very strong and significant for live funds. Not surprisingly, dead funds exhibit lower initial alphas than live funds, and the dead fund initial alphas are not statistically significant. While the age coefficient is negative for dead funds, the age effect is also not statistically significant. In general, the alpha series for dead funds is quite noisy, consistent with funds that die being much more volatile than funds that survive. This evidence is complementary to that of Chan, et al. (2006), which finds that young funds with poor performance are more likely to die than others. An open and interesting question is whether investors anticipate that poorly performing new funds are likely to fail and hasten their demise by withdrawing funds. Panel D then separates the sample into live single funds, live multi-funds, dead single funds, and dead multi-funds. The age effect is significant for live funds, whether from single or multi-funds, and is particularly strong for the single funds. B. Backfilled vs. Non-Backfilled Funds Table 4 presents alphas in event time for the backfilled funds. Panel A presents annual alphas from the first reported performance after inception. This sample fully incorporates all of the backfilled data. Not surprisingly, alphas are large but decreasing for the first four years of inception. To see how much of this performance is due to firms backfilling positive returns, we reexamine our backfilled sample by truncating all monthly return observations prior to the fund starting to report performance to the database. Since the database reports the date that the fund was added to the database, we treat all monthly return observations prior to this date as backfilled and eliminate them. We then compute alphas for the remaining (non-backfilled) observations in event time, where the event is the fund being added to the database. These results are presented in Panel 20 It is worth noting that a random examination of fund manager backgrounds reveals that virtually all emerging fund managers have some prior trading experience either at an investment bank, institutional money management firm, 22

23 B of Table 4. The alphas plummet relative to those in Panel A. Interestingly, alphas are also lower than for the purely non-backfilled funds in Table 2, suggesting that there are important return effects during the first few years of a fund s life that can be missed by simply truncating all backfilled returns. Panel C reports the alphas of backfilled hedge funds, separated into multi-product funds (1274) and single-product funds (992). The ratio of single-product funds in the backfilled sample is 992/2266=44%, which is slightly higher than that in the non-backfilled sample, 347/923=38%. Thus, single-product funds are somewhat more susceptible to backfill returns than are multi-product funds. In addition, we find that the performance in the first (backfilled) year is greater for singleproduct funds, by 12.68% % = 3.07%, and remains much higher for the first four years of life. Panel D reports the alphas of backfilled hedge funds separated into multi-product and singleproduct funds after eliminating all of the backfilled returns (i.e., the returns prior to the fund being added to the database). Eliminating the backfilled period for the backfilled funds dramatically shrinks the alphas, with single funds outperforming the multi-product funds in the first year only by 2.28% % = 1.32%. The conclusion for the backfilled sample is that single funds seem to perform better, but most of this performance is attributable to greater backfill bias for single funds. Figure 1 displays the cumulative alphas aligned by event time for our non-backfilled sample. The initial performance is very strong, and then tapers off as the fund ages. Note that the performance in the first year is not driven by the first three months alone, which should dispel concerns about the remaining 82-day median period between the performance start date and the date added to the database. Also note that the beginning of the line is rather smooth, due to the large number of funds in the series. Towards the end, however, the line is much more irregular. This mutual fund, or as a junior manager at another hedge fund. 23